fix: fix function calling with the latest litellm version

docs/prompt.md

@@ -67,13 +67,6 @@ Banks supports the following ones, specific for prompt engineering.
         show_signature_annotations: false
         heading_level: 3
 
-::: banks.extensions.docs.generate
-    options:
-        show_root_full_path: false
-        show_symbol_type_heading: false
-        show_signature_annotations: false
-        heading_level: 3
-
 ### `canary_word`
 
 Insert into the prompt a canary word that can be checked later with `Prompt.canary_leaked()`

src/banks/extensions/completion.py

@@ -89,15 +89,16 @@ def _do_completion(self, model_name, caller):
         Helper callback.
         """
         messages, tools = self._body_to_messages(caller())
-        messages_as_dict = [m.model_dump() for m in messages]
+        message_dicts = [m.model_dump() for m in messages]
+        tool_dicts = [t.model_dump() for t in tools] or None
 
-        response = cast(ModelResponse, completion(model=model_name, messages=messages_as_dict, tools=tools or None))
+        response = cast(ModelResponse, completion(model=model_name, messages=message_dicts, tools=tool_dicts))
         choices = cast(list[Choices], response.choices)
         tool_calls = choices[0].message.tool_calls
         if not tool_calls:
             return choices[0].message.content
 
-        messages.append(choices[0].message)  # type:ignore
+        message_dicts.append(choices[0].message.model_dump())
         for tool_call in tool_calls:
             if not tool_call.function.name:
                 msg = "Malformed response: function name is empty"
@@ -107,14 +108,13 @@ def _do_completion(self, model_name, caller):
 
             function_args = json.loads(tool_call.function.arguments)
             function_response = func(**function_args)
-            messages.append(
+            message_dicts.append(
                 ChatMessage(
                     tool_call_id=tool_call.id, role="tool", name=tool_call.function.name, content=function_response
-                )
+                ).model_dump()
             )
 
-        messages_as_dict = [m.model_dump() for m in messages]
-        response = cast(ModelResponse, completion(model=model_name, messages=messages_as_dict))
+        response = cast(ModelResponse, completion(model=model_name, messages=message_dicts, tools=tool_dicts))
         choices = cast(list[Choices], response.choices)
         return choices[0].message.content
 
@@ -123,45 +123,51 @@ async def _do_completion_async(self, model_name, caller):
         Helper callback.
         """
         messages, tools = self._body_to_messages(caller())
+        message_dicts = [m.model_dump() for m in messages]
+        tool_dicts = [t.model_dump() for t in tools] or None
 
-        response = cast(ModelResponse, await acompletion(model=model_name, messages=messages, tools=tools))
+        response = cast(ModelResponse, await acompletion(model=model_name, messages=message_dicts, tools=tool_dicts))
         choices = cast(list[Choices], response.choices)
         tool_calls = choices[0].message.tool_calls or []
         if not tool_calls:
             return choices[0].message.content
 
-        messages.append(choices[0].message)  # type:ignore
+        message_dicts.append(choices[0].message.model_dump())
         for tool_call in tool_calls:
             if not tool_call.function.name:
                 msg = "Function name is empty"
                 raise LLMError(msg)
 
             func = self._get_tool_callable(tools, tool_call)
 
-            messages.append(
+            message_dicts.append(
                 ChatMessage(
                     tool_call_id=tool_call.id,
                     role="tool",
                     name=tool_call.function.name,
                     content=func(**json.loads(tool_call.function.arguments)),
-                )
+                ).model_dump()
             )
 
-        response = cast(ModelResponse, await acompletion(model=model_name, messages=messages))
+        response = cast(ModelResponse, await acompletion(model=model_name, messages=message_dicts, tools=tool_dicts))
         choices = cast(list[Choices], response.choices)
         return choices[0].message.content
 
     def _body_to_messages(self, body: str) -> tuple[list[ChatMessage], list[Tool]]:
+        """Converts each line in the body of a block into a chat message."""
         body = body.strip()
         messages = []
         tools = []
         for line in body.split("\n"):
             try:
+                # Try to parse a chat message
                 messages.append(ChatMessage.model_validate_json(line))
             except ValidationError:  # pylint: disable=R0801
                 try:
+                    # If not a chat message, try to parse a tool
                     tools.append(Tool.model_validate_json(line))
                 except ValidationError:
+                    # Give up
                     pass
 
         if not messages:

tests/e2e/test_function_calling.py

@@ -0,0 +1,54 @@
+import platform
+
+import pytest
+
+from banks import Prompt
+
+from .conftest import anthropic_api_key_set, openai_api_key_set
+
+
+def get_laptop_info():
+    """Get information about the user laptop.
+
+    For example, it returns the operating system and version, along with hardware and network specs."""
+    return str(platform.uname())
+
+
+@pytest.mark.e2e
+@openai_api_key_set
+def test_function_call_openai():
+    p = Prompt("""
+    {% set response %}
+    {% completion model="gpt-3.5-turbo-0125" %}
+        {% chat role="user" %}{{ query }}{% endchat %}
+        {{ get_laptop_info | tool }}
+    {% endcompletion %}
+    {% endset %}
+
+    {# the variable 'response' contains the result #}
+
+    {{ response }}
+    """)
+
+    res = p.text({"query": "Can you guess the name of my laptop?", "get_laptop_info": get_laptop_info})
+    assert res
+
+
+@pytest.mark.e2e
+@anthropic_api_key_set
+def test_function_call_anthropic():
+    p = Prompt("""
+    {% set response %}
+    {% completion model="claude-3-5-sonnet-20240620" %}
+        {% chat role="user" %}{{ query }}{% endchat %}
+        {{ get_laptop_info | tool }}
+    {% endcompletion %}
+    {% endset %}
+
+    {# the variable 'response' contains the result #}
+
+    {{ response }}
+    """)
+
+    res = p.text({"query": "Can you guess the name of my laptop? Use tools.", "get_laptop_info": get_laptop_info})
+    assert res

tests/test_completion.py

@@ -125,7 +125,9 @@ async def test__do_completion_async_no_tools(ext, mocked_choices_no_tools):
         mocked_completion.return_value.choices = mocked_choices_no_tools
         await ext._do_completion_async("test-model", lambda: '{"role":"user", "content":"hello"}')
         mocked_completion.assert_called_with(
-            model="test-model", messages=[ChatMessage(role="user", content="hello")], tools=[]
+            model="test-model",
+            messages=[{"role": "user", "content": "hello", "tool_call_id": None, "name": None}],
+            tools=None,
         )
 
 
@@ -143,24 +145,27 @@ def test__do_completion_with_tools(ext, mocked_choices_with_tools):
         calls = mocked_completion.call_args_list
         assert len(calls) == 2  # complete query, complete with tool results
         assert len(calls[0].kwargs["tools"]) == 2
-        assert "tools" not in calls[1].kwargs
         for m in calls[1].kwargs["messages"]:
             if type(m) is ChatMessage:
                 assert m.role == "tool"
                 assert m.name == "get_current_weather"
 
 
 @pytest.mark.asyncio
-async def test__do_completion_async_with_tools(ext, mocked_choices_with_tools):
+async def test__do_completion_async_with_tools(ext, mocked_choices_with_tools, tools):
     ext._get_tool_callable = mock.MagicMock(return_value=lambda location, unit: f"I got {location} with {unit}")
-    ext._body_to_messages = mock.MagicMock(return_value=(["message1", "message2"], ["tool1", "tool2"]))
+    ext._body_to_messages = mock.MagicMock(
+        return_value=(
+            [ChatMessage(role="user", content="message1"), ChatMessage(role="user", content="message2")],
+            tools,
+        )
+    )
     with mock.patch("banks.extensions.completion.acompletion") as mocked_completion:
         mocked_completion.return_value.choices = mocked_choices_with_tools
         await ext._do_completion_async("test-model", lambda: '{"role":"user", "content":"hello"}')
         calls = mocked_completion.call_args_list
         assert len(calls) == 2  # complete query, complete with tool results
-        assert calls[0].kwargs["tools"] == ["tool1", "tool2"]
-        assert "tools" not in calls[1].kwargs
+        assert calls[0].kwargs["tools"] == [t.model_dump() for t in tools]
         for m in calls[1].kwargs["messages"]:
             if type(m) is ChatMessage:
                 assert m.role == "tool"
@@ -190,7 +195,9 @@ async def test__do_completion_async_no_prompt_no_tools(ext, mocked_choices_no_to
         mocked_completion.return_value.choices = mocked_choices_no_tools
         await ext._do_completion_async("test-model", lambda: '{"role":"user", "content":"hello"}')
         mocked_completion.assert_called_with(
-            model="test-model", messages=[ChatMessage(role="user", content="hello")], tools=[]
+            model="test-model",
+            messages=[{"role": "user", "content": "hello", "tool_call_id": None, "name": None}],
+            tools=None,
         )